Comparer des méthodes

	Recherche quantitative exploratoire hiérarchique	Échantillonnage en grappes
Domaine	Conception de la recherche	Méthodologie d'enquête
Famille	Process / pipeline	Process / pipeline
Année d'origine	mid-20th century onward	Early-to-mid 20th century; canonical treatment 1953/1977
Auteur d'origine	Developed from survey research traditions (Kish, 1965; Babbie, 1990s)	Formalized by William G. Cochran; roots in early 20th-century U.S. Census Bureau survey practice
Type	Quantitative observational and survey design	Probability sampling design
Source fondatrice	Creswell, J. W. (2014). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (4th ed.). Sage Publications. ISBN: 978-1452226101	Cochran, W. G. (1977). Sampling Techniques (3rd ed.). Wiley. ISBN: 978-0471162407
Alias	stratified exploratory survey design, hierarchical survey research, multilevel exploratory quantitative design, hierarchical descriptive-quantitative design	cluster random sampling, area sampling, one-stage cluster sampling
Apparentées	2	5
Résumé	Hierarchical exploratory quantitative research is a survey and observational design that structures both sampling and analysis across nested population levels — such as students within classrooms within schools — to explore patterns, distributions, and relationships in numerical data without a pre-specified directional hypothesis. It is oriented toward discovery and description rather than confirmation, making it appropriate early in a research programme when the phenomenon is not yet well-mapped.	Cluster sampling is a probability sampling technique in which the population is divided into naturally occurring groups (clusters), a random sample of clusters is selected, and all — or a random subset of — members within each selected cluster are studied. It is especially practical when a complete population list is unavailable or when units are geographically dispersed, making individual random selection prohibitively expensive. One-stage cluster sampling surveys every member of selected clusters; two-stage designs add a second random draw within clusters.